TorQ-bench 0.1.1

Tensor Operations for Research in Quantum systems - Benchmarking.

TorQ-bench

Benchmarks and PennyLane comparisons for TorQ.

TorQ-bench is a small companion package that lets you run the same layer logic in TorQ and PennyLane to compare outputs and timing. It intentionally keeps PennyLane out of the core TorQ package.

Install

From source (recommended while developing):

pip install -e .[pennylane]

If published on PyPI:

pip install torq-bench[pennylane]

TorQ-bench depends on torq-quantum>=0.1.2. PennyLane is optional and only required for the comparison wrappers.

Note: the PyPI distribution is torq-quantum, while the Python import package remains torq.

Quickstart: compare TorQ vs PennyLane

import torch
from torq.QLayer import QLayer
from torq_bench import PennyLaneQLayer

n_qubits = 4
n_layers = 2
x = torch.rand(8, n_qubits)

torq_layer = QLayer(n_qubits=n_qubits, n_layers=n_layers)
pl_layer = PennyLaneQLayer(
    n_qubits=n_qubits,
    n_layers=n_layers,
    pennylane_dev_name="default.qubit",
)

y_torq = torq_layer(x)
y_pl = pl_layer(x)

Notes:

• PennyLaneQLayer currently supports only ansatz_name="basic_entangling".
• data_reupload_every is not supported in PennyLaneQLayer.

Using PennyLaneComparison directly

import torch
from torq_bench import PennyLaneComparison

n_qubits = 4
n_layers = 2
weights = torch.rand(n_layers, n_qubits, 3)
x = torch.rand(n_qubits)

qc = PennyLaneComparison(n_qubits=n_qubits, n_layers=n_layers, weights=weights)
circuit = qc.circuit_strongly_entangling()
y = circuit(x)

Run the built-in demo

The comparison module includes a demo that builds and draws several circuits. It uses qml.draw_mpl, so you will need matplotlib installed.

python -m torq_bench.PennyLaneComparison

License

MIT